Overview

Dataset statistics

Number of variables20
Number of observations50
Missing cells80
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory167.6 B

Variable types

Numeric4
Categorical14
Text1
Unsupported1

Alerts

SLE_CHNNEL_URL has constant value ""Constant
TICKET_OPTN_CD is highly overall correlated with SEQ_NO and 15 other fieldsHigh correlation
TRRSRT_NM is highly overall correlated with SEQ_NO and 14 other fieldsHigh correlation
TURSM_COURS_CD is highly overall correlated with SEQ_NO and 14 other fieldsHigh correlation
PRCHS_ID is highly overall correlated with SEQ_NO and 15 other fieldsHigh correlation
TICKET_USE_PLACE_NM is highly overall correlated with PRCHS_DT and 14 other fieldsHigh correlation
TURSM_COURS_NM is highly overall correlated with SEQ_NO and 14 other fieldsHigh correlation
TICKET_SLE_BRAND_NM is highly overall correlated with SEQ_NO and 14 other fieldsHigh correlation
USER_KEY_NM is highly overall correlated with SEQ_NO and 15 other fieldsHigh correlation
TICKET_OPTN_NM is highly overall correlated with SEQ_NO and 15 other fieldsHigh correlation
TRRSRT_ADDR is highly overall correlated with SEQ_NO and 15 other fieldsHigh correlation
TICKET_SLE_CHNNEL_NM is highly overall correlated with SEQ_NO and 15 other fieldsHigh correlation
TRRSRT_CD is highly overall correlated with SEQ_NO and 14 other fieldsHigh correlation
SEQ_NO is highly overall correlated with PRCHS_DT and 14 other fieldsHigh correlation
PRCHS_DT is highly overall correlated with SEQ_NO and 14 other fieldsHigh correlation
TICKET_VALID_DT is highly overall correlated with SEQ_NO and 15 other fieldsHigh correlation
USE_DT is highly overall correlated with SEQ_NO and 9 other fieldsHigh correlation
TICKET_USGSTT_NM is highly overall correlated with SEQ_NO and 15 other fieldsHigh correlation
USE_DT has 30 (60.0%) missing valuesMissing
USE_CANCL_DT has 50 (100.0%) missing valuesMissing
SEQ_NO has unique valuesUnique
TICKET_ID has unique valuesUnique
USE_CANCL_DT is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:04:02.488580
Analysis finished2023-12-10 10:04:10.210849
Duration7.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SEQ_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179452.5
Minimum179428
Maximum179477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:04:10.364032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum179428
5-th percentile179430.45
Q1179440.25
median179452.5
Q3179464.75
95-th percentile179474.55
Maximum179477
Range49
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation14.57738
Coefficient of variation (CV)8.1232525 × 10-5
Kurtosis-1.2
Mean179452.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum8972625
Variance212.5
MonotonicityStrictly increasing
2023-12-10T19:04:10.754929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179428 1
 
2.0%
179466 1
 
2.0%
179456 1
 
2.0%
179457 1
 
2.0%
179458 1
 
2.0%
179459 1
 
2.0%
179460 1
 
2.0%
179461 1
 
2.0%
179462 1
 
2.0%
179463 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
179428 1
2.0%
179429 1
2.0%
179430 1
2.0%
179431 1
2.0%
179432 1
2.0%
179433 1
2.0%
179434 1
2.0%
179435 1
2.0%
179436 1
2.0%
179437 1
2.0%
ValueCountFrequency (%)
179477 1
2.0%
179476 1
2.0%
179475 1
2.0%
179474 1
2.0%
179473 1
2.0%
179472 1
2.0%
179471 1
2.0%
179470 1
2.0%
179469 1
2.0%
179468 1
2.0%

USER_KEY_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7
18 
1f112e73b81a9a8e386a96c410918e508244a4a3
16 
e79b6f696c4aca9bf56a24a3b34280d40362efe3
12 
e43582c2d0008590cfa43e51cacd7c0d088df5d1
2 
0f54d0222aac4123cfd4e123b02d13243c18bf8b
2 

Length

Max length40
Median length40
Mean length40
Min length40

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowe43582c2d0008590cfa43e51cacd7c0d088df5d1
2nd rowe43582c2d0008590cfa43e51cacd7c0d088df5d1
3rd rowe79b6f696c4aca9bf56a24a3b34280d40362efe3
4th rowe79b6f696c4aca9bf56a24a3b34280d40362efe3
5th rowe79b6f696c4aca9bf56a24a3b34280d40362efe3

Common Values

ValueCountFrequency (%)
609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7 18
36.0%
1f112e73b81a9a8e386a96c410918e508244a4a3 16
32.0%
e79b6f696c4aca9bf56a24a3b34280d40362efe3 12
24.0%
e43582c2d0008590cfa43e51cacd7c0d088df5d1 2
 
4.0%
0f54d0222aac4123cfd4e123b02d13243c18bf8b 2
 
4.0%

Length

2023-12-10T19:04:11.016205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:11.308420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7 18
36.0%
1f112e73b81a9a8e386a96c410918e508244a4a3 16
32.0%
e79b6f696c4aca9bf56a24a3b34280d40362efe3 12
24.0%
e43582c2d0008590cfa43e51cacd7c0d088df5d1 2
 
4.0%
0f54d0222aac4123cfd4e123b02d13243c18bf8b 2
 
4.0%

PRCHS_ID
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
ODTK-20200802-FCGRGJ-RNM
18 
ODTK-20200802-KQELRL-EQJ
16 
ODTK-20200802-ZKJTIY-HNT
6 
ODTK-20200802-OFELZH-AMA
6 
ODTK-20200802-AUUVYF-DAX
2 

Length

Max length24
Median length24
Mean length24
Min length24

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowODTK-20200802-AUUVYF-DAX
2nd rowODTK-20200802-AUUVYF-DAX
3rd rowODTK-20200802-ZKJTIY-HNT
4th rowODTK-20200802-ZKJTIY-HNT
5th rowODTK-20200802-ZKJTIY-HNT

Common Values

ValueCountFrequency (%)
ODTK-20200802-FCGRGJ-RNM 18
36.0%
ODTK-20200802-KQELRL-EQJ 16
32.0%
ODTK-20200802-ZKJTIY-HNT 6
 
12.0%
ODTK-20200802-OFELZH-AMA 6
 
12.0%
ODTK-20200802-AUUVYF-DAX 2
 
4.0%
ODTK-20200802-BJFDLE-HAX 2
 
4.0%

Length

2023-12-10T19:04:11.597426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:11.850371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
odtk-20200802-fcgrgj-rnm 18
36.0%
odtk-20200802-kqelrl-eqj 16
32.0%
odtk-20200802-zkjtiy-hnt 6
 
12.0%
odtk-20200802-ofelzh-ama 6
 
12.0%
odtk-20200802-auuvyf-dax 2
 
4.0%
odtk-20200802-bjfdle-hax 2
 
4.0%

TICKET_ID
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-10T19:04:12.207884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length13.14
Min length13

Characters and Unicode

Total characters657
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row4203026669785
2nd row7962678307545
3rd row1274996522190
4th row7927265907822
5th row2212187496448
ValueCountFrequency (%)
4203026669785 1
 
2.0%
1084701383074 1
 
2.0%
1882532433165 1
 
2.0%
3115682770207 1
 
2.0%
6073962357587 1
 
2.0%
1349052009307 1
 
2.0%
4843098578366 1
 
2.0%
200802bjfdlehax00168 1
 
2.0%
4440241441053 1
 
2.0%
9971337494095 1
 
2.0%
Other values (40) 40
80.0%
2023-12-10T19:04:12.923161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 73
11.1%
3 73
11.1%
1 73
11.1%
0 69
10.5%
7 64
9.7%
5 64
9.7%
8 62
9.4%
2 59
9.0%
6 57
8.7%
9 54
8.2%
Other values (9) 9
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 648
98.6%
Uppercase Letter 9
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 73
11.3%
3 73
11.3%
1 73
11.3%
0 69
10.6%
7 64
9.9%
5 64
9.9%
8 62
9.6%
2 59
9.1%
6 57
8.8%
9 54
8.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
11.1%
J 1
11.1%
F 1
11.1%
D 1
11.1%
L 1
11.1%
E 1
11.1%
H 1
11.1%
A 1
11.1%
X 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 648
98.6%
Latin 9
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
4 73
11.3%
3 73
11.3%
1 73
11.3%
0 69
10.6%
7 64
9.9%
5 64
9.9%
8 62
9.6%
2 59
9.1%
6 57
8.8%
9 54
8.3%
Latin
ValueCountFrequency (%)
B 1
11.1%
J 1
11.1%
F 1
11.1%
D 1
11.1%
L 1
11.1%
E 1
11.1%
H 1
11.1%
A 1
11.1%
X 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 73
11.1%
3 73
11.1%
1 73
11.1%
0 69
10.5%
7 64
9.7%
5 64
9.7%
8 62
9.4%
2 59
9.0%
6 57
8.7%
9 54
8.2%
Other values (9) 9
 
1.4%

TICKET_USGSTT_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
취소
20 
사용
20 
사용만료
10 

Length

Max length4
Median length2
Mean length2.4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취소
2nd row취소
3rd row사용만료
4th row사용만료
5th row사용

Common Values

ValueCountFrequency (%)
취소 20
40.0%
사용 20
40.0%
사용만료 10
20.0%

Length

2023-12-10T19:04:13.361180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:13.938051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
취소 20
40.0%
사용 20
40.0%
사용만료 10
20.0%

TURSM_COURS_CD
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
PROD00000428
30 
PROD00000076
18 
PROD00000362
 
2

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPROD00000428
2nd rowPROD00000428
3rd rowPROD00000428
4th rowPROD00000428
5th rowPROD00000428

Common Values

ValueCountFrequency (%)
PROD00000428 30
60.0%
PROD00000076 18
36.0%
PROD00000362 2
 
4.0%

Length

2023-12-10T19:04:14.578105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:14.975148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
prod00000428 30
60.0%
prod00000076 18
36.0%
prod00000362 2
 
4.0%

TURSM_COURS_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
경북나드리 울진(덕구온천 스파월드+관광지패키지)
30 
경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)
18 
경북나드리 경주(경주월드+엑스포공원)
 
2

Length

Max length38
Median length26
Mean length30.08
Min length20

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경북나드리 울진(덕구온천 스파월드+관광지패키지)
2nd row경북나드리 울진(덕구온천 스파월드+관광지패키지)
3rd row경북나드리 울진(덕구온천 스파월드+관광지패키지)
4th row경북나드리 울진(덕구온천 스파월드+관광지패키지)
5th row경북나드리 울진(덕구온천 스파월드+관광지패키지)

Common Values

ValueCountFrequency (%)
경북나드리 울진(덕구온천 스파월드+관광지패키지) 30
60.0%
경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관) 18
36.0%
경북나드리 경주(경주월드+엑스포공원) 2
 
4.0%

Length

2023-12-10T19:04:15.301228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:15.524825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경북나드리 50
38.5%
울진(덕구온천 30
23.1%
스파월드+관광지패키지 30
23.1%
상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관 18
 
13.8%
경주(경주월드+엑스포공원 2
 
1.5%

TICKET_OPTN_CD
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
OPTI00001413
18 
OPTI00001347
8 
OPTI00001344
8 
OPTI00001346
8 
OPTI00001349
6 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOPTI00001347
2nd rowOPTI00001347
3rd rowOPTI00001347
4th rowOPTI00001347
5th rowOPTI00001347

Common Values

ValueCountFrequency (%)
OPTI00001413 18
36.0%
OPTI00001347 8
16.0%
OPTI00001344 8
16.0%
OPTI00001346 8
16.0%
OPTI00001349 6
 
12.0%
OPTI00001254 2
 
4.0%

Length

2023-12-10T19:04:15.767720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:16.019420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
opti00001413 18
36.0%
opti00001347 8
16.0%
opti00001344 8
16.0%
opti00001346 8
16.0%
opti00001349 6
 
12.0%
opti00001254 2
 
4.0%

TICKET_OPTN_NM
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2.소인
18 
2-1.성인
8 
1-1.성인
8 
1-3.소인
8 
2-3.소인
6 

Length

Max length8
Median length6
Mean length5.36
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2-1.성인
2nd row2-1.성인
3rd row2-1.성인
4th row2-1.성인
5th row2-1.성인

Common Values

ValueCountFrequency (%)
2.소인 18
36.0%
2-1.성인 8
16.0%
1-1.성인 8
16.0%
1-3.소인 8
16.0%
2-3.소인 6
 
12.0%
3.4시권 성인 2
 
4.0%

Length

2023-12-10T19:04:16.264554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:16.558056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.소인 18
34.6%
2-1.성인 8
15.4%
1-1.성인 8
15.4%
1-3.소인 8
15.4%
2-3.소인 6
 
11.5%
3.4시권 2
 
3.8%
성인 2
 
3.8%

TRRSRT_CD
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
P00000001066
10 
P00000001007
5 
P00000001008
5 
P00000001005
5 
P00000001820
4 
Other values (10)
21 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique2 ?
Unique (%)4.0%

Sample

1st rowP00000001066
2nd rowP00000001066
3rd rowP00000001057
4th rowP00000001057
5th rowP00000001820

Common Values

ValueCountFrequency (%)
P00000001066 10
20.0%
P00000001007 5
10.0%
P00000001008 5
10.0%
P00000001005 5
10.0%
P00000001820 4
 
8.0%
P00000001822 4
 
8.0%
P00000001009 3
 
6.0%
P00000001057 2
 
4.0%
P00000001059 2
 
4.0%
P00000001054 2
 
4.0%
Other values (5) 8
16.0%

Length

2023-12-10T19:04:16.860975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p00000001066 10
20.0%
p00000001007 5
10.0%
p00000001008 5
10.0%
p00000001005 5
10.0%
p00000001820 4
 
8.0%
p00000001822 4
 
8.0%
p00000001009 3
 
6.0%
p00000001057 2
 
4.0%
p00000001059 2
 
4.0%
p00000001054 2
 
4.0%
Other values (5) 8
16.0%

TRRSRT_NM
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
[울진]커피방
10 
[상주]국립낙동강생물자원관_소인
5 
[상주]국제승마장_소인
5 
[상주]박물관_소인
5 
[울진]스파월드_성수기/성인
4 
Other values (10)
21 

Length

Max length20
Median length17
Mean length13.06
Min length7

Unique

Unique2 ?
Unique (%)4.0%

Sample

1st row[울진]커피방
2nd row[울진]커피방
3rd row[울진]과학체험관_성인
4th row[울진]과학체험관_성인
5th row[울진]스파월드_성수기/성인

Common Values

ValueCountFrequency (%)
[울진]커피방 10
20.0%
[상주]국립낙동강생물자원관_소인 5
10.0%
[상주]국제승마장_소인 5
10.0%
[상주]박물관_소인 5
10.0%
[울진]스파월드_성수기/성인 4
 
8.0%
[울진]스파월드_성수기/소인 4
 
8.0%
[상주]수상레저센터_소인 3
 
6.0%
[울진]과학체험관_성인 2
 
4.0%
[울진]과학체험관_소인 2
 
4.0%
[울진]엑스포공원 곤충여행체험관_성인 2
 
4.0%
Other values (5) 8
16.0%

Length

2023-12-10T19:04:17.193140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
울진]커피방 10
17.2%
울진]엑스포공원 8
13.8%
상주]국립낙동강생물자원관_소인 5
8.6%
상주]국제승마장_소인 5
8.6%
상주]박물관_소인 5
8.6%
울진]스파월드_성수기/성인 4
 
6.9%
울진]스파월드_성수기/소인 4
 
6.9%
상주]수상레저센터_소인 3
 
5.2%
울진]과학체험관_성인 2
 
3.4%
울진]과학체험관_소인 2
 
3.4%
Other values (6) 10
17.2%

TRRSRT_ADDR
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
경북 울진군 근남면 산포3길 162 2층
10 
경북 울진군 북면 덕구온천로 924
8 
경북 울진군 근남면 수산리 63
8 
경상북도 상주시 도남2길 137 국립낙동강생물자원관
5 
경상북도 상주시 사벌국면 화달리 산23 상주국제승마장
5 
Other values (5)
14 

Length

Max length29
Median length22
Mean length20.8
Min length14

Unique

Unique2 ?
Unique (%)4.0%

Sample

1st row경북 울진군 근남면 산포3길 162 2층
2nd row경북 울진군 근남면 산포3길 162 2층
3rd row경북 울진군 울진읍 연지길 30
4th row경북 울진군 울진읍 연지길 30
5th row경북 울진군 북면 덕구온천로 924

Common Values

ValueCountFrequency (%)
경북 울진군 근남면 산포3길 162 2층 10
20.0%
경북 울진군 북면 덕구온천로 924 8
16.0%
경북 울진군 근남면 수산리 63 8
16.0%
경상북도 상주시 도남2길 137 국립낙동강생물자원관 5
10.0%
경상북도 상주시 사벌국면 화달리 산23 상주국제승마장 5
10.0%
경상북도 상주시 사벌국면 경천로 684 5
10.0%
경북 울진군 울진읍 연지길 30 4
 
8.0%
경북 상주시 용마로 366 3
 
6.0%
경북 경주시 천군동 148-4 1
 
2.0%
경북 경주시 천군동 191-5 1
 
2.0%

Length

2023-12-10T19:04:17.527046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:17.904976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경북 35
 
13.5%
울진군 30
 
11.5%
근남면 18
 
6.9%
상주시 18
 
6.9%
경상북도 15
 
5.8%
산포3길 10
 
3.8%
162 10
 
3.8%
2층 10
 
3.8%
사벌국면 10
 
3.8%
63 8
 
3.1%
Other values (21) 96
36.9%

TICKET_SLE_CHNNEL_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
쿠팡
30 
옥션
16 
티몬
 
2
G마켓
 
2

Length

Max length3
Median length2
Mean length2.04
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row티몬
2nd row티몬
3rd row쿠팡
4th row쿠팡
5th row쿠팡

Common Values

ValueCountFrequency (%)
쿠팡 30
60.0%
옥션 16
32.0%
티몬 2
 
4.0%
G마켓 2
 
4.0%

Length

2023-12-10T19:04:18.274297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:18.490944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
쿠팡 30
60.0%
옥션 16
32.0%
티몬 2
 
4.0%
g마켓 2
 
4.0%

SLE_CHNNEL_URL
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
-
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 50
100.0%

Length

2023-12-10T19:04:18.729747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:18.896849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50
100.0%

PRCHS_DT
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200802 × 1013
Minimum2.0200802 × 1013
Maximum2.0200802 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:04:19.040229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200802 × 1013
5-th percentile2.0200802 × 1013
Q12.0200802 × 1013
median2.0200802 × 1013
Q32.0200802 × 1013
95-th percentile2.0200802 × 1013
Maximum2.0200802 × 1013
Range1785
Interquartile range (IQR)1662

Descriptive statistics

Standard deviation757.89467
Coefficient of variation (CV)3.7518049 × 10-11
Kurtosis-1.7010728
Mean2.0200802 × 1013
Median Absolute Deviation (MAD)342
Skewness0.44986937
Sum1.0100401 × 1015
Variance574404.34
MonotonicityIncreasing
2023-12-10T19:04:19.247905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20200802095620 18
36.0%
20200802094200 16
32.0%
20200802093858 6
 
12.0%
20200802093958 6
 
12.0%
20200802093835 2
 
4.0%
20200802094700 2
 
4.0%
ValueCountFrequency (%)
20200802093835 2
 
4.0%
20200802093858 6
 
12.0%
20200802093958 6
 
12.0%
20200802094200 16
32.0%
20200802094700 2
 
4.0%
20200802095620 18
36.0%
ValueCountFrequency (%)
20200802095620 18
36.0%
20200802094700 2
 
4.0%
20200802094200 16
32.0%
20200802093958 6
 
12.0%
20200802093858 6
 
12.0%
20200802093835 2
 
4.0%

TICKET_VALID_DT
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200928 × 1013
Minimum2.0200812 × 1013
Maximum2.020113 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:04:19.437677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200812 × 1013
5-th percentile2.0200812 × 1013
Q12.0200812 × 1013
median2.0200814 × 1013
Q32.020113 × 1013
95-th percentile2.020113 × 1013
Maximum2.020113 × 1013
Range3.1811406 × 108
Interquartile range (IQR)3.181053 × 108

Descriptive statistics

Standard deviation1.5317632 × 108
Coefficient of variation (CV)7.5826378 × 10-6
Kurtosis-1.7079671
Mean2.0200928 × 1013
Median Absolute Deviation (MAD)1982209
Skewness0.6004653
Sum1.0100464 × 1015
Variance2.3462985 × 1016
MonotonicityNot monotonic
2023-12-10T19:04:19.629623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20201130235959 18
36.0%
20200814104109 7
 
14.0%
20200814104110 7
 
14.0%
20200812121901 6
 
12.0%
20200812122123 6
 
12.0%
20200823235959 4
 
8.0%
20200812130659 2
 
4.0%
ValueCountFrequency (%)
20200812121901 6
 
12.0%
20200812122123 6
 
12.0%
20200812130659 2
 
4.0%
20200814104109 7
 
14.0%
20200814104110 7
 
14.0%
20200823235959 4
 
8.0%
20201130235959 18
36.0%
ValueCountFrequency (%)
20201130235959 18
36.0%
20200823235959 4
 
8.0%
20200814104110 7
 
14.0%
20200814104109 7
 
14.0%
20200812130659 2
 
4.0%
20200812122123 6
 
12.0%
20200812121901 6
 
12.0%

USE_DT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)45.0%
Missing30
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean2.0200805 × 1013
Minimum2.0200802 × 1013
Maximum2.0200807 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:04:19.827966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200802 × 1013
5-th percentile2.0200802 × 1013
Q12.0200802 × 1013
median2.0200806 × 1013
Q32.0200807 × 1013
95-th percentile2.0200807 × 1013
Maximum2.0200807 × 1013
Range5009002
Interquartile range (IQR)4958755

Descriptive statistics

Standard deviation2264628.5
Coefficient of variation (CV)1.1210585 × 10-7
Kurtosis-1.850897
Mean2.0200805 × 1013
Median Absolute Deviation (MAD)1510174
Skewness-0.27855763
Sum4.040161 × 1014
Variance5.1285422 × 1012
MonotonicityNot monotonic
2023-12-10T19:04:20.093807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20200807105020 4
 
8.0%
20200807113239 4
 
8.0%
20200804101110 3
 
6.0%
20200802114901 2
 
4.0%
20200802115123 2
 
4.0%
20200807123903 2
 
4.0%
20200804101109 1
 
2.0%
20200802123659 1
 
2.0%
20200802164759 1
 
2.0%
(Missing) 30
60.0%
ValueCountFrequency (%)
20200802114901 2
4.0%
20200802115123 2
4.0%
20200802123659 1
 
2.0%
20200802164759 1
 
2.0%
20200804101109 1
 
2.0%
20200804101110 3
6.0%
20200807105020 4
8.0%
20200807113239 4
8.0%
20200807123903 2
4.0%
ValueCountFrequency (%)
20200807123903 2
4.0%
20200807113239 4
8.0%
20200807105020 4
8.0%
20200804101110 3
6.0%
20200804101109 1
 
2.0%
20200802164759 1
 
2.0%
20200802123659 1
 
2.0%
20200802115123 2
4.0%
20200802114901 2
4.0%

USE_CANCL_DT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size582.0 B

TICKET_SLE_BRAND_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
울진 e누리
30 
상주 e누리
18 
경주 e누리
 
2

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울진 e누리
2nd row울진 e누리
3rd row울진 e누리
4th row울진 e누리
5th row울진 e누리

Common Values

ValueCountFrequency (%)
울진 e누리 30
60.0%
상주 e누리 18
36.0%
경주 e누리 2
 
4.0%

Length

2023-12-10T19:04:20.310545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:20.501016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e누리 50
50.0%
울진 30
30.0%
상주 18
 
18.0%
경주 2
 
2.0%

TICKET_USE_PLACE_NM
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
-
20 
[울진] 덕구온천
8 
[울진] 커피방
8 
[울진] 과학 체험관
4 
[울진] 엑스포공원 곤충체험여행관
4 
Other values (3)
6 

Length

Max length18
Median length16
Mean length7.1
Min length1

Unique

Unique2 ?
Unique (%)4.0%

Sample

1st row-
2nd row-
3rd row[울진] 과학 체험관
4th row[울진] 과학 체험관
5th row[울진] 덕구온천

Common Values

ValueCountFrequency (%)
- 20
40.0%
[울진] 덕구온천 8
 
16.0%
[울진] 커피방 8
 
16.0%
[울진] 과학 체험관 4
 
8.0%
[울진] 엑스포공원 곤충체험여행관 4
 
8.0%
[울진] 엑스포공원 아쿠아리움 4
 
8.0%
[경주] 엑스포공원 1
 
2.0%
[경주] 경주월드 1
 
2.0%

Length

2023-12-10T19:04:20.714930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:20.949526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울진 28
30.4%
20
21.7%
엑스포공원 9
 
9.8%
덕구온천 8
 
8.7%
커피방 8
 
8.7%
과학 4
 
4.3%
체험관 4
 
4.3%
곤충체험여행관 4
 
4.3%
아쿠아리움 4
 
4.3%
경주 2
 
2.2%

Interactions

2023-12-10T19:04:08.399338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:05.682268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:06.440119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:07.202836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:08.587154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:05.906305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:06.598399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:07.490091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:08.744674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:06.071710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:06.781150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:07.666427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:08.958609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:06.239124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:07.002816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:07.847867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:04:21.137015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOUSER_KEY_NMPRCHS_IDTICKET_IDTICKET_USGSTT_NMTURSM_COURS_CDTURSM_COURS_NMTICKET_OPTN_CDTICKET_OPTN_NMTRRSRT_CDTRRSRT_NMTRRSRT_ADDRTICKET_SLE_CHNNEL_NMPRCHS_DTTICKET_VALID_DTUSE_DTTICKET_SLE_BRAND_NMTICKET_USE_PLACE_NM
SEQ_NO1.0000.9570.8661.0000.8210.8510.8510.8610.8610.9030.9030.9210.8010.9230.9930.7260.8510.768
USER_KEY_NM0.9571.0001.0001.0000.7851.0001.0000.9150.9150.9480.9480.9671.0001.0001.0000.9231.0000.848
PRCHS_ID0.8661.0001.0001.0000.9721.0001.0000.9940.9940.8920.8920.8331.0001.0001.0000.9231.0000.815
TICKET_ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
TICKET_USGSTT_NM0.8210.7850.9721.0001.0000.9160.9160.9590.9590.9830.9830.9100.5130.7540.643NaN0.9160.930
TURSM_COURS_CD0.8511.0001.0001.0000.9161.0001.0001.0001.0001.0001.0001.0000.7601.0001.0000.3831.0000.943
TURSM_COURS_NM0.8511.0001.0001.0000.9161.0001.0001.0001.0001.0001.0001.0000.7601.0001.0000.3831.0000.943
TICKET_OPTN_CD0.8610.9150.9941.0000.9591.0001.0001.0001.0000.9310.9310.8160.9401.0001.0000.6181.0000.794
TICKET_OPTN_NM0.8610.9150.9941.0000.9591.0001.0001.0001.0000.9310.9310.8160.9401.0001.0000.6181.0000.794
TRRSRT_CD0.9030.9480.8921.0000.9831.0001.0000.9310.9311.0001.0001.0000.8400.9411.0000.9521.0000.993
TRRSRT_NM0.9030.9480.8921.0000.9831.0001.0000.9310.9311.0001.0001.0000.8400.9411.0000.9521.0000.993
TRRSRT_ADDR0.9210.9670.8331.0000.9101.0001.0000.8160.8161.0001.0001.0000.8580.9481.0000.7541.0000.962
TICKET_SLE_CHNNEL_NM0.8011.0001.0001.0000.5130.7600.7600.9400.9400.8400.8400.8581.0001.0000.9170.9230.7600.945
PRCHS_DT0.9231.0001.0001.0000.7541.0001.0001.0001.0000.9410.9410.9481.0001.0001.0000.9231.0000.992
TICKET_VALID_DT0.9931.0001.0001.0000.6431.0001.0001.0001.0001.0001.0001.0000.9171.0001.000NaN1.0000.986
USE_DT0.7260.9230.9231.000NaN0.3830.3830.6180.6180.9520.9520.7540.9230.923NaN1.0000.3830.952
TICKET_SLE_BRAND_NM0.8511.0001.0001.0000.9161.0001.0001.0001.0001.0001.0001.0000.7601.0001.0000.3831.0000.943
TICKET_USE_PLACE_NM0.7680.8480.8151.0000.9300.9430.9430.7940.7940.9930.9930.9620.9450.9920.9860.9520.9431.000
2023-12-10T19:04:21.508418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TICKET_OPTN_CDTRRSRT_NMTURSM_COURS_CDPRCHS_IDTICKET_USE_PLACE_NMTURSM_COURS_NMTICKET_SLE_BRAND_NMUSER_KEY_NMTICKET_USGSTT_NMTICKET_OPTN_NMTRRSRT_ADDRTICKET_SLE_CHNNEL_NMTRRSRT_CD
TICKET_OPTN_CD1.0000.6780.9680.8820.5860.9680.9680.8600.7301.0000.5800.8200.678
TRRSRT_NM0.6781.0000.8630.6020.8870.8630.8630.6210.7320.6780.9350.5721.000
TURSM_COURS_CD0.9680.8631.0000.9680.9001.0001.0000.9780.6450.9680.9230.8030.863
PRCHS_ID0.8820.6020.9681.0000.6160.9680.9680.9890.7690.8820.6040.9780.602
TICKET_USE_PLACE_NM0.5860.8870.9000.6161.0000.9000.9000.7090.8810.5860.8600.6600.887
TURSM_COURS_NM0.9680.8631.0000.9680.9001.0001.0000.9780.6450.9680.9230.8030.863
TICKET_SLE_BRAND_NM0.9680.8631.0000.9680.9001.0001.0000.9780.6450.9680.9230.8030.863
USER_KEY_NM0.8600.6210.9780.9890.7090.9780.9781.0000.7820.8600.7050.9890.621
TICKET_USGSTT_NM0.7300.7320.6450.7690.8810.6450.6450.7821.0000.7300.8020.5080.732
TICKET_OPTN_NM1.0000.6780.9680.8820.5860.9680.9680.8600.7301.0000.5800.8200.678
TRRSRT_ADDR0.5800.9350.9230.6040.8600.9230.9230.7050.8020.5801.0000.6600.935
TICKET_SLE_CHNNEL_NM0.8200.5720.8030.9780.6600.8030.8030.9890.5080.8200.6601.0000.572
TRRSRT_CD0.6781.0000.8630.6020.8870.8630.8630.6210.7320.6780.9350.5721.000
2023-12-10T19:04:21.795552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOPRCHS_DTTICKET_VALID_DTUSE_DTUSER_KEY_NMPRCHS_IDTICKET_USGSTT_NMTURSM_COURS_CDTURSM_COURS_NMTICKET_OPTN_CDTICKET_OPTN_NMTRRSRT_CDTRRSRT_NMTRRSRT_ADDRTICKET_SLE_CHNNEL_NMTICKET_SLE_BRAND_NMTICKET_USE_PLACE_NM
SEQ_NO1.0000.9580.8200.6490.6960.6730.6510.7100.7100.6560.6560.5650.5650.5370.6230.7100.482
PRCHS_DT0.9581.0000.8610.3980.9890.9780.7380.9890.9890.9780.9780.7430.7430.8130.8220.9890.801
TICKET_VALID_DT0.8200.8611.0000.5450.9680.9570.9050.9900.9900.9570.9570.8540.8540.9130.5660.9900.846
USE_DT0.6490.3980.5451.0000.6640.6181.0000.4030.4030.5730.5730.4260.4260.6450.6640.4030.598
USER_KEY_NM0.6960.9890.9680.6641.0000.9890.7820.9780.9780.8600.8600.6210.6210.7050.9890.9780.709
PRCHS_ID0.6730.9780.9570.6180.9891.0000.7690.9680.9680.8820.8820.6020.6020.6040.9780.9680.616
TICKET_USGSTT_NM0.6510.7380.9051.0000.7820.7691.0000.6450.6450.7300.7300.7320.7320.8020.5080.6450.881
TURSM_COURS_CD0.7100.9890.9900.4030.9780.9680.6451.0001.0000.9680.9680.8630.8630.9230.8031.0000.900
TURSM_COURS_NM0.7100.9890.9900.4030.9780.9680.6451.0001.0000.9680.9680.8630.8630.9230.8031.0000.900
TICKET_OPTN_CD0.6560.9780.9570.5730.8600.8820.7300.9680.9681.0001.0000.6780.6780.5800.8200.9680.586
TICKET_OPTN_NM0.6560.9780.9570.5730.8600.8820.7300.9680.9681.0001.0000.6780.6780.5800.8200.9680.586
TRRSRT_CD0.5650.7430.8540.4260.6210.6020.7320.8630.8630.6780.6781.0001.0000.9350.5720.8630.887
TRRSRT_NM0.5650.7430.8540.4260.6210.6020.7320.8630.8630.6780.6781.0001.0000.9350.5720.8630.887
TRRSRT_ADDR0.5370.8130.9130.6450.7050.6040.8020.9230.9230.5800.5800.9350.9351.0000.6600.9230.860
TICKET_SLE_CHNNEL_NM0.6230.8220.5660.6640.9890.9780.5080.8030.8030.8200.8200.5720.5720.6601.0000.8030.660
TICKET_SLE_BRAND_NM0.7100.9890.9900.4030.9780.9680.6451.0001.0000.9680.9680.8630.8630.9230.8031.0000.900
TICKET_USE_PLACE_NM0.4820.8010.8460.5980.7090.6160.8810.9000.9000.5860.5860.8870.8870.8600.6600.9001.000

Missing values

2023-12-10T19:04:09.278823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:04:09.985789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SEQ_NOUSER_KEY_NMPRCHS_IDTICKET_IDTICKET_USGSTT_NMTURSM_COURS_CDTURSM_COURS_NMTICKET_OPTN_CDTICKET_OPTN_NMTRRSRT_CDTRRSRT_NMTRRSRT_ADDRTICKET_SLE_CHNNEL_NMSLE_CHNNEL_URLPRCHS_DTTICKET_VALID_DTUSE_DTUSE_CANCL_DTTICKET_SLE_BRAND_NMTICKET_USE_PLACE_NM
0179428e43582c2d0008590cfa43e51cacd7c0d088df5d1ODTK-20200802-AUUVYF-DAX4203026669785취소PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013472-1.성인P00000001066[울진]커피방경북 울진군 근남면 산포3길 162 2층티몬-2020080209383520200823235959<NA><NA>울진 e누리-
1179429e43582c2d0008590cfa43e51cacd7c0d088df5d1ODTK-20200802-AUUVYF-DAX7962678307545취소PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013472-1.성인P00000001066[울진]커피방경북 울진군 근남면 산포3길 162 2층티몬-2020080209383520200823235959<NA><NA>울진 e누리-
2179430e79b6f696c4aca9bf56a24a3b34280d40362efe3ODTK-20200802-ZKJTIY-HNT1274996522190사용만료PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013472-1.성인P00000001057[울진]과학체험관_성인경북 울진군 울진읍 연지길 30쿠팡-2020080209385820200812121901<NA><NA>울진 e누리[울진] 과학 체험관
3179431e79b6f696c4aca9bf56a24a3b34280d40362efe3ODTK-20200802-ZKJTIY-HNT7927265907822사용만료PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013472-1.성인P00000001057[울진]과학체험관_성인경북 울진군 울진읍 연지길 30쿠팡-2020080209385820200812121901<NA><NA>울진 e누리[울진] 과학 체험관
4179432e79b6f696c4aca9bf56a24a3b34280d40362efe3ODTK-20200802-ZKJTIY-HNT2212187496448사용PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013472-1.성인P00000001820[울진]스파월드_성수기/성인경북 울진군 북면 덕구온천로 924쿠팡-202008020938582020081212190120200802114901<NA>울진 e누리[울진] 덕구온천
5179433e79b6f696c4aca9bf56a24a3b34280d40362efe3ODTK-20200802-ZKJTIY-HNT0311192912405사용PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013472-1.성인P00000001820[울진]스파월드_성수기/성인경북 울진군 북면 덕구온천로 924쿠팡-202008020938582020081212190120200802114901<NA>울진 e누리[울진] 덕구온천
6179434e79b6f696c4aca9bf56a24a3b34280d40362efe3ODTK-20200802-ZKJTIY-HNT7585469631647사용만료PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013472-1.성인P00000001066[울진]커피방경북 울진군 근남면 산포3길 162 2층쿠팡-2020080209385820200812121901<NA><NA>울진 e누리[울진] 커피방
7179435e79b6f696c4aca9bf56a24a3b34280d40362efe3ODTK-20200802-ZKJTIY-HNT9324154365682사용만료PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013472-1.성인P00000001066[울진]커피방경북 울진군 근남면 산포3길 162 2층쿠팡-2020080209385820200812121901<NA><NA>울진 e누리[울진] 커피방
8179436e79b6f696c4aca9bf56a24a3b34280d40362efe3ODTK-20200802-OFELZH-AMA3014255338832사용만료PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013492-3.소인P00000001059[울진]과학체험관_소인경북 울진군 울진읍 연지길 30쿠팡-2020080209395820200812122123<NA><NA>울진 e누리[울진] 과학 체험관
9179437e79b6f696c4aca9bf56a24a3b34280d40362efe3ODTK-20200802-OFELZH-AMA0333215476596사용만료PROD00000428경북나드리 울진(덕구온천 스파월드+관광지패키지)OPTI000013492-3.소인P00000001059[울진]과학체험관_소인경북 울진군 울진읍 연지길 30쿠팡-2020080209395820200812122123<NA><NA>울진 e누리[울진] 과학 체험관
SEQ_NOUSER_KEY_NMPRCHS_IDTICKET_IDTICKET_USGSTT_NMTURSM_COURS_CDTURSM_COURS_NMTICKET_OPTN_CDTICKET_OPTN_NMTRRSRT_CDTRRSRT_NMTRRSRT_ADDRTICKET_SLE_CHNNEL_NMSLE_CHNNEL_URLPRCHS_DTTICKET_VALID_DTUSE_DTUSE_CANCL_DTTICKET_SLE_BRAND_NMTICKET_USE_PLACE_NM
40179468609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM4577844959314취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001008[상주]국제승마장_소인경상북도 상주시 사벌국면 화달리 산23 상주국제승마장쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
41179469609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM6800258208533취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001008[상주]국제승마장_소인경상북도 상주시 사벌국면 화달리 산23 상주국제승마장쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
42179470609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM0242103757767취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001005[상주]박물관_소인경상북도 상주시 사벌국면 경천로 684쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
43179471609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM9935401157713취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001005[상주]박물관_소인경상북도 상주시 사벌국면 경천로 684쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
44179472609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM0138184005397취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001005[상주]박물관_소인경상북도 상주시 사벌국면 경천로 684쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
45179473609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM8819258347998취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001005[상주]박물관_소인경상북도 상주시 사벌국면 경천로 684쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
46179474609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM4438367437446취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001005[상주]박물관_소인경상북도 상주시 사벌국면 경천로 684쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
47179475609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM4307030655363취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001009[상주]수상레저센터_소인경북 상주시 용마로 366쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
48179476609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM1882532433165취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001009[상주]수상레저센터_소인경북 상주시 용마로 366쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-
49179477609ddbe4d4e19e0e831d9e763ec0a226f9fe6db7ODTK-20200802-FCGRGJ-RNM8196173215151취소PROD00000076경북나드리 상주(승마체험+수상레저+생물자원관+자전거박물관+상주박물관)OPTI000014132.소인P00000001009[상주]수상레저센터_소인경북 상주시 용마로 366쿠팡-2020080209562020201130235959<NA><NA>상주 e누리-